International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January ISSN

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1 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January Improving the productivity of small and medium scale industries using linear programming model By 1 Ezeliora Chukwuemeka Daniel; 2 Umeh Maryrose Ngozi; 2 Mbeledeogu Njide N.; 3 Ezeokonkwo John U. 1 Department of Industrial and Production Engineering, 2 Department of Computer Science, 3 Department of Building 123 Nnamdi Azikiwe University Awka, Anambra State, Nigeria ABSTRACT: Productivity improvement in small and medium scale enterprise will develop both the economic condition and the industry on its side. During these research operation research techniques where employed to Golden plastic industry limited (GPIL) and a linear programming technique was used in maximizing profit. Productivity therefore improve by applying the linear programming instead of the normal trial and error been employed by the managers of the industry. Key words: Optimization, Linear Programming, Plastic, Productivity, Quantity and Decision Variables Introduction Golden Plastic Industry Limited, Emene produces eight different products. It has the necessary physical facilities to apply linear programming techniques in determining the quantity combination of the different types of products that maximize profit (optimal product mix). Computer facilities are adequate, yet the firm uses the trial-and-error method in determining its product mix. Golden Plastic Industry Limited is chosen for this study for two main reasons. First, it uses the trial-and-error method in arriving at major management decisions even when the researchers feels that a linear programming approach would have given a better result and improve the productivity of the industry. Secondly, Golden Plastic Industry Limited produces eight different products which makes the determination of the quantity combinations of the products produced (product mix) an important and major management decision.

2 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January Researchers then applied linear programming to determine a new quantity combination. The total contribution to profit of each of the products for the month using the new quantity will now be compared with the total profit contribution made by the former product mix determined by the trial-and-error method. The problems encountered in the process will be noted and from personal interviews and relevant records, other peculiarities shall be established. Radharamanan et al. applied Kaizen philosophy for continuous improvement and to develop the products with higher Objectives of Study: The objective of quality, lower cost, and higher this study is to apply productivity productivity that meet the customer improvement model (PIM) to improve requirements whereas Huang et al. productivity of small and medium scale applied effectiveness metric and industries. simulation analysis for improving Review of Relevance Study: Productivity improvement stimulates researchers to search for methods that can achieve the increase in the productivity which in turn supports business main objectives. In this field of research, many researchers focused on improving process rather than employee performance in order to get more gains in productivity. One of these researchers was Yung who developed a new method to improve the manufacturing process productivity based on rearranging the sequence of tools and techniques by considering the coordination of information flow and selecting only the suitable tools for the specialized problems. The goal of the method was achieved but still there are some factors affecting the workers performance. Radharamanan et al. and Huang et al. also, did not consider the factors affecting the workers performance. manufacturing productivity. In addition to these efforts, Andris and Benjamin used the intimate relationship between four techniques and they proposed an integrated model that shows significant improvement on both quality and productivity of the product and process. The techniques used are Statistical Process Control (SPC), the seven basic tools (histogram, check sheet, cause-and-effect diagram, control

3 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January chart, Pareto Chart, flow process chart, scatter diagram), KAIZEN (Japanese term of continuous improvement), and Total Quality Management (TQM) principles. However, the main drawback of their model is that SPC, KAIZEN and TQM are very time-consuming and bulky methods. resources such as market demand, production process and equipment storage capacity, raw material available, and so on. They further posit that programming implies planning and by linearity is meant a mathematical expression in which the expressions among the variables are linear. According to Charles, Cooper and Henderson (1963), this is known as Dowing (1992) advocates that the Lagrangian method should be used for optimization problem, and can be any optimization subject to a single approached using mathematical inequality constraint, the Graphic programming. They further refer to approach for optimization subject to linear programming as a uni-objective only two inequality constraints, and the constrained optimization technique. This linear programming model for is because, according to them, it seeks a optimization subject to many inequality single objective of either minimizing or constraints. Supporting this view, maximizing unknown variables in a Dwivedi (28) posits that linear model. In line with this, Gupta and Hira (29) argue that linear programming deals with linear optimization of a function of variables known as objective function subject to set of linear equations programming is of great use in making business decision because it helps in measuring complex economic relations and thereby, provides an optimum solution to the problem of resource and /or inequalities known as allocation. According to him, linear constraints. The objective function may be profit, cost, production capacity or programming technique thus, bridges the gap between abstract economic theories any other measure of effectiveness and managerial decision-making. which is to be obtained in the best Furthermore, he stressed that any linear possible or optimal manner. The programming equation should have three constraints may be imposed by different specifications, namely: objective

4 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January function specification, constraint equation specification, and nonnegativity requirement. Corroborating this view, several authors (Dowling, 1992, Dwivedi, 28, Koutsoyiannis, 1979, Henderson and Quandt, 23, etc) have given the general specification of the linear programming model. Adeyemo and Otiero (29) also tried to demonstrate that the linear programming maintenance crew out of the 19 model can be extended beyond the realms of Management Sciences and employees are needed in that section to effectively carry out maintenance jobs in organizational decision departments to the industry. But in their own other areas such as Physical and contributions, Nedim et al (22) tried to Environmental Sciences. They used the demonstrate that risk analysis is application of Differential Evolution necessary in order to maximize (DE) and Linear Programming (LP) to resources allocation efficiency and maximize total income (in South African minimize the effects of risk Rand ZAR) of 25 ha planting area where 16 crops are planted and constrained by water availability (using only 1mm3 of irrigation water). It is found that a total income of ZAR 46,6,2 can be derived using linear programming. Ten strategies of DE are tested with this problem varying the population size (NP), crossover constant (CR) and weighing factor (F). It is found that strategy 1, DE/rand-1-bin, with values of NP, CR and F of 16,.95 and.5 respectively obtains the best solution most efficiently. Kareem and Aderoba (28) tried to show the effectiveness of adopting the linear programming model in maintenance and manpower planning using data from a cocoa processing industry in Akure, Ondo State of Nigeria. The result shows that only four environment. They used data from a sample of a company s products taking risk into account as the objective function. The result suggests that producing 5 units of X1 generates 36% loss possibility. If decision makers aim risk not to exceed certain limits, then, variances should be used as constraints. The model suggests that producing 3 units of X1 will decrease the objective function from $432 to $287.

5 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January This paper is a contribution in this field of research. It focuses on improving process productivity as well as worker performance. It proposes mathematical model that improves the productivity without increasing the risk and the fatigue that affects the worker performance by enabling the user to select the best technique that achieves this aim among set of candidate techniques. necessary information to be collected Research Methods: For this study both from the appropriate and different Quantitative and Qualitative research departments of the industries which we method are employed to get an insight use as the research case study. on the production system of the industries and device a means to improve productivity of the industries. Analyzing Techniques: These was The Qualitative method used will be based on developing a mathematical basically industrial production decision model that aims at improving improvement tools. the productivity of the production Research Design: In the design adopted for this research work, we made use of both Quantitative and Qualitative method of productivity improvement to model the production system of small and medium scale industries in eastern part of Nigeria. The Qualitative methods used are work study, method study and time measurement. These methods where used to carry out Qualitative analysis in order to device a model to improve productivity of the industries. Three industries around eastern Nigeria was used to carry out these research due to the complexity involve in getting small scale industries to carry out research work at these level. The sources of data collection used are both primary and secondary and it allows for process by selecting the best techniques to perform significant operations without increasing the risk, the fatigue, cost and time associated with the implementation of the selected techniques. Linear Programming: Linear programming is the name of a branch of applied mathematics that deals with solving optimization problems of a

6 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January particular form. Linear programming problems consist of a linear cost function (consisting of a certain number of variables) which is to be minimized or maximized subject to a certain number of constraints. The constraints are linear inequalities of the variables used in the cost function. The cost function is also sometimes called the objective function. Linear programming is closely related to linear algebra; the most noticeable difference is that linear programming However, the following procedures are often uses inequalities in the problem necessary when formulating Linear statement rather than equalities. Programming (LP) problems: write The objective is to maximize or down decisions variables of the problem; minimize a single objective function formulate the objective function in terms relative to a set of constraints. of decision variables; formulate the other conditions/constraints of the problem to A mathematical program is linear, if which the optimization process is subjected to, such as resources F (x 1, x 2,,x n ) and each G (x 1, limitation, market constraints as linear x 2,,x n ) (i =2,3,,n) are linear in equations in terms of the variables; add each of their argument. non-negativity conditions/ constraints- That is, the considerations that negative values of physical variable in most cases do not F(x 1,x 2,,x n ) = c 1 x 1 +c 2 x 2 + +c n x n have any valid physical interpretation. g (x 1,x 2,,x n ) = a 11 x 1 +a 12 x 2 + +a in x n c i and a ij (i = 1,2,..n; j = 1,2,,n) are known constants. All linear programming problems have the following properties in common: all seek to optimize some quantity. This property is referred to as the objective function. There are constraints, which limit the degree to which the objective can be pursued; and there must be alternatives to choose from. The objectives and constraints in linear programming must be expressed in terms of linear equation or inequalities. Summarily, the objective function, the set of constraints and the non-negativity together form the linear programming model of the problem.

7 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January Model Formulation: When there are n choice variables and m constraints, the productivity improvement model takes the general form with a linear objective function, a set of linear inequality constraints and a set of non-negativity restrictions as its major ingredients. The generalized n variable linear programme can be stated as below: Maximize π = c 1 x 1 + c 2 x c n x n (objective function) where c i, a ij and r i are given constants. The variables x 1,x 2,.., x n are called decision or structural variables. The problem is to find the values of the decision variables (x 1, x 2,, x n ) which maximize the objective function π subject to the m constraints and the nonnegativity restriction on the x j variable. The resulting set of decision variables which maximize the objective function is called the optimal solution. This procedure is called Simplex Algorithm). Subject to a 11 x 1 + a 12 x 2 + The model for use in the present study + a 1n x n r 1 is: a 21 x 1 + a 22 x 2 + MAXIMIZE Z = P 1 X 1 + P 2 X 2 + P 3 X a 2n x n r 2 P 4 X P 8 X 8 P 5 X 5 + P 6 X 6 + P 7 X SUBJECT TO: C 11 X 1 + C 12 X 2 + C 13 X 3 + C 14 X 4 C 15 X 5 + C 16 X 6 + C 17 X 7 + C 18 X 8 B 1 a m1 x 1 + a m2 x a mn x n r m x j (j = 1,2,..n) (nonnegativity restrictions) + C 24 X 4 C 21 X 1 + C 22 X 2 + C 23 X 3

8 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January C 28 X 8 B 2 C 25 X 5 + C 26 X 6 + C 27 X 7 T t 5 x 5 + t 6 x 6 + t 7 x 7 + t 8 x 8 C 31 X 1 + C 32 X2 + C 33 X 3 X i (where I = 1,2,.,8) + C 34 X 4 C 35 X 5 + C 36 X 6 + C 37 X 7 + C 38 X 8 B 3 C 41 X 1 + C 42 X 2 + C 43 X 3 + C 44 X 4 C 45 X 5 + C 46 X 6 + C 47 X 7 + C 48 X 8 B 4 where: Z = total profit contribution of the various products of GPIL for the month of May, 212. P 1. 8 = profit contribution coefficients i.e. the numerical values that express C 51 X 1 + C 52 X 2 + C 53 X 3 + C 54 X 4 per unit contribution to the profit equation. C 55 X 5 + C 56 X 6 + C 57 X 7 X + C 58 X 8 B 1 8 = the set of unknown we are 5 seeking to determine i.e. the various C 61 X 1 + C 62 X 2 + C 63 X 3 products + C 54 X 4 C 65 X 5 + C 66 X 6 + C 67 X 7 + C 68 X 8 B 6 C 71 X 1 + C 72 X 2 + C 73 X 3 + C 74 X 4 C 75 X 5 + C 76 X 6 + C 77 X 7 + C 78 X 8 B 7 produce by firm. C 1.8 = technological coefficients i.e. the numerical values that express the per unit usage of the right hand side. B 1. 8 = the resource values that we seek to fully utilize. t 1 x 1 + t 2 x 2 + t 3 x 3 + t 4 x 4

9 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January T = the maximum labour time available for production within the production period (in hours or mins). t 1.8 = the labour time required to produce one unit of the various products of Industry. 75mm by 5.4m light pressure pipes 63mm by 5.4m thick pressure pipes 5mm by 5.4m waste pipes 4mm by 5.4m thick pressure pipes 32mm by 5.4m thick pressure pipes 25mm by 5.4m conduit pipes Estimates of the variables will be 2mm by 5.4m thick pressure pipes. presented in tables. The optimum values of the different brands produced by the In order to produce these pipes, firm will show the combination (product the firm requires different materials in mix) obtained through the application of different combinations. It requires linear programming model. The tables machines of different types and sizes, will also show those resources that are skilled and unskilled labour and raw abundant and those that are in short-fall. materials. But for the purpose of this research work, we shall concentrate on Data Collection and its Products: raw materials and two factors: man and Golden Plastic Industry Limited (GPIL), machine hours needed for production Emene is engaged in the production of from the 1st day to the 31st day of May, different sizes, shapes, and lengths of 212. Other factors are held constant. plastic pipes known as PVC pipes. These The major raw materials used by the products are differentiated by their sizes, firm in the production of the above thickness and length. The pressure pipes products include: used for circulating tap water is usually thicker than the waste pipes used in Resin (the major raw material) water system toilets and bathrooms. The Calcium carbonate products of GPIL include the following: Titanium oxide (Tio 2 ) Stabilizer 11mm by 5.4m thick pressure pipes Cast

10 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January Carbon black Blend The raw materials are mixed in different proportions and as such take different percentages of the production costs. dioxide Stabilizer 5 Cast 35 Carbon black 7 Blend 26 Labour 648 time(hrs) Table 1: Raw material to the quantity of each pipe specification RAW X1 X2 X3 X4 X5 X6 X7 X8 MATERIAL Resin Calcium carbonate Titanium dioxide 5 Stabilizer Cast X 6 + Carbon black Blend ,5, Labour time in hours Table 2: Cost of various raw materials provided by GPIL RAW TOTAL COST MATERIAL OF EACH RAW MATERIAL Resin 35 Calcium carbonate 15 Titanium 3 Data Analysis: Unit Profit Contribution by the various Products (see below). Based on all the information provided, Golden Plastic can be translated into the model above, thus: MAXIMIZE Z = 3X 1 + 4X 2 +25X X 4 + 3X X 6 +15X 7 +35X 8 SUBJECT TO: 516X X X 3 + 4X X X X 6 18X X X X X X 6 + 6X 7 + 3X 8 1,5, 6X 1 + 4X X 3 + 7X X X 6 + 2X 7 + 1X 8 3,

11 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January X X X 3 + 5X X X X X 8 5, 48X X 2 + 3X 3 + 4X X X X 7 + 8X 8 35, 84X X X 3 + 6X X X X X 8 7, Results Obtained 24X X X X X X mm by 5.4m thick 2,6, 1.4X X 8.213X X X X X X X X Matlab program will be employed to solve the mathematical problem the result will then be shown below; Table 3: Unit profit contribution by various products S/N PRODUCT PRODUCTION UNIT SELLING UNIT COST PER PRICE (NAIRA) PROFIT UNIT (NAIRA) (NAIRA) 1 11mm by m thick pressure pipe. 2 75mm by m light pressure pipe. 3 63mm by m thick pressure pipe. 4 5mm by m waste pipe. 5 4mm by m thick pressure pipe. 6 32mm by m thick pressure pipe. 7 25mm by m conduit pipe. 8 2mm by m thick pressure pipe. Table 4: The primal optimal values of decision variables S/N DECISION VARIABLES OPTIMUM VALUES pressure pipes x mm by 5.4m light pressure pipes x mm by 5.4 thick pressure pipes x 3 4 5mm by 5.4m waste pipes x 4 5 4mm by 5.4m thick pressure pipes x mm by 5.4m thick pressure pipes x mm by 5.4m conduit pipes x 7 8 2mm by 5.4m thick pressure pipes x 8 114, , Table 5: The dual resources value (shadow prices) RESOURCES VALUE S (SHADO W PRICES )

12 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January Resin Y 1 Calcium carbonate Y 2 Tio 2 Y 3 2,742,29 1 Stabilizer Y 4 Cast Y 5 Carbon Black Y 6 Blend Y 7 Labour time (hours) Y Optimum Value W Table 6: Post optimality A(unit profit increases) Decision variable Optimal values Profit Table 8: Post optimum c(resource budget and work time increased) Decisionvariable Optimal values X 1 X 1 X 2 X 2 X 3 X 3 X 4 X 5 X 4 X 5 X 6 X 6 X X X X Optimum Value Optimum Profit Table 7: Post optimality B (unit profit decreased) DECISION VARIABLES OPTIMAL VALUE X 1 X 2 X 3 X 4 X 5 X 6 X X Optimum 982,268.8 Table 9: Post optimality D resource budget and working capital Decision variable X 1 Optimal values X 2 X 3 X 4 X 5 X 6

13 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January X 7 57,138 X 8 Optimal Profit Decision Variables; X 1 = the quantity of 11mm by 5.4m thick pressure pipes to be produced. X 2 = the Quantity of 75mm by 5.4m light pressure pipes to be produced. Profit contribution coefficients represent the numerical values that express the per unit contribution to the profit equation (Z). P 1 = the average net contribution by one unit of 11mm by 5.4m thick pressure pipe. P 2 = the average net contribution by one unit of 75mm by 5.4m light pressure pipe. X 3 = the quantity of 63mm by 5.4m P 3 = the average net contribution by one thick pressure pipes to be produced. X 4 unit of 63mm by 5.4m thick pressure = the quantity of 5mm by 5.4m pipe. waste pipes to be produced. P 4 = the average net contribution by one X 5 = the quantity of 4mm by 5.4m unit of 5mm by 5.4m waste pipe. thick pressure pipes to be produced. P 5 = the average net contribution by one X 6 = the quantity of 32mm by 5.4m unit of 4mm by 5.4m thick pressure thick pressure pipes to be produced. pipe. X 7 = the quantity of 25mm by 5.4m conduit pipes to be produced. X 8 = the quantity of 2mm by 5.4m thick pressure pipes to be produced. Profit Contribution Coefficients (Given Constants) P 6 = the average net contribution by one unit of 32mm by 5.4m thick pressure pipe. P 7 = the average net contribution by one unit of 25mm by 5.4m conduit pipe. P 8 = the average net contribution by one unit of 2mm by 5.4m thick pressure pipe.

14 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January Technology Coefficients (given constants) In the model, technological coefficients represent the numerical values that express the per unit usage of the various raw materials in the production of the various products. For instance; C 11 = Cost of Resin in producing one unit of 11mm by 5.4m thick pressure pipe. t 2 = the labour time (in hours) required to produce one unit of 75mm by 5.4m light pressure pipe. T = the maximum labour time available for production within the production period etc. Right-hand Side values (given constants) These represent the resource values that we seek to fully utilize. For instance; B C 21 = Cost of Calcium Carbonate used 1 = the amount of money available within the production period for the in producing one unit of 11mm by 5.4m purchase of Resin. thick pressure pipe. B C 71 = Cost of Blend used in producing 2 = the amount of money available within the production period for the one unit of 11mm by 5.4m thick purchase of Calcium carbonate pressure pipe. C 12 = Cost of Resin used in producing one unit of 75mm by 5.4m light pressure pipe etc. Labour Time Labour time here represents the labour time required to produce one unit of the various products of GFIL. For instance; t 1 = the labour time (in hours) required to produce one unit of 11mm by 5.4m thick pressure pipe. Discussion: The various estimated values of the optimization model for Golden Plastic Industry Limited are presented in 6 different tables (tables 4 to 9). Tables 4 and 5 present the primal and dual values of the estimates respectively while tables 6 to 9 present the computer print-out of the post optimality values of the optimization model.

15 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January From table, the optimal solution of the problem is zero production for 6 out of the 8 products of GPIL. The production level of 25mm by 5.4m conduit pipes and 2mm by 5.4m thick pressure pipes yielded 114,317.2 and 7, respectively while the objective function yielded N1,964,532. But Golden Plastic could only make a total profit of N982,656 from 1st to 31st May, 212. This obvious reduction in stabilizer (Y4), cast (Y5), carbon black (Y6) and blend (Y7) are scarce due to their zero values. This means that they were consumed completely by the activities of the model. Therefore for the optimal solution to be improved, the scarce resources should be increased since any increase of the abundant resources will only make them more abundant without affecting the optimum solution. profit is partly attributed to random selection of product mix and resource The optimal values of the products; 25mm by 5.4m conduit pipes allocation. Application of linear and 2mm by 5.4m thick pressure pipes programming would have indicated to increased and decreased with the post management that the company should optimality C when the resource budget produce only 114,317.2 of 25mm by and working time are increased and 5.4m conduit pipes and of 2mm by 5.4m thick pressure pipes in order to make the maximum profit of decreased respectively. However, it is important to note that the increase in the N1,964,537 and thereof improve resources budget and working time in productivity of (GPIL). The other post optimality C yields a smaller products should not be produced since optimal profit compared to the their production adds more to cost than to profit. The dual solution in table 6 reaffirms the authenticity of the optimal value of the objective function in the primal result. It also shows that while tio 2 (Y3) and labour time (Y8) are abundant due to their positive values, resin (Y1), calcium carbonate (Y2), maximum profit of 1,964,537 naira in table 5 which suggests that there is a limit to which resources can be increased in other to achieve the objective function. Conclusion: The study was successfully determined the product mix of Golden Plastic Industry Limited, Emene. In the

16 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January process, the optimal quantities of the References various PVC pipes to be produced within the study period in order to improve productivity were established. Also the status of the resources and the unit worth Adeyemo, J. and F. Otiero, 29, Optimizing Planting Areas using Differential Evolution and Linear of each resource to the objective Programming International function were known. This is the Journal of Physical Sciences, advantage of going beyond mere vol.4 pp knowledge of existing decision making Charles, Cooper & Henderson, 1963, tools to actual practical proof of its An Introduction to Linear workability. Another issue becomes how the Programming, John Willy, New York managerial cadre of the productive firms Dowing, E.T., 1992, Theory and at Emene Industrial Layout could be Problems of Introduction to exposed to the rigorous steps involved in Mathematical Economics, arriving at the optimal values of the Schaum s Outline Series, linear programming model. From the McGraw-Hall Inc., New York researchers personal observations in the course of this study, Golden Plastic Industry Limited, Emene was relatively few persons skilled in the Operations Research techniques who also possess a broad understanding of business environment and knowledge of the managerial roles and functions. As such, the firm should rely on outside consultants to bring this and other techniques to bear on management s decision problems. This can go a long way to assisting the management. Emory, W and P. Niland, 1998, Making Management Decisions, Oxford & IBH, New Delhi Gupta, P.K and D.S. Hira, 29, Operations Research, S. Chand Company Limited, New Delhi Henderson, J and R. Quandt, 23, Microeconomic Theory: A Mathematical Approach, McGraw-Hill Book Co., Tata, New Delhi

17 International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January Hillier, F and G. Lieberman, 21, Introduction to Operations Research, McGraw-Hill Book Co., New York Kareem, B and A. Aderoba, 28, Linear Programming based Effective Maintenance and Manpower Planning Strategy, International Journal of the Murugan, N and S. Manivel, 29, Profit Planning of an NGO run Enterprises using Linear Programming Approach, International Research Journal of Finance and Economics, Issue 23, Nedim et al, 22, A sample of Determination of Product Computer, the Internet and combination with Linear Management, vol.16 pp Programming in Risk Environment, European Journal Koji, T and R. Gunner, 22, Image of Economics, Finance and Reconstruction by Linear Administrative Sciences, ISSN Programming, Max Planck , Issue 3 Institute for Biological Cybernetics, Spemannstr, 38, 7276, Germany